Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer

Abualigah, Laith; Elaziz, Mohamed Abd; Sumari, Putra; Geem, Zong Woo; Gandomi, Amir H.

doi:10.1016/j.eswa.2021.116158

Cited by 934 publications

(342 citation statements)

References 83 publications

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“…This section presents the original Reptile Search Algorithm (RSA) and its procedure. The basic Reptile Search Algorithm (RSA) is described in its exploration (global search) and exploitation (local search) stages, which were inspired by the encircling mechanics, hunting processes, and social behavior of crocodiles in real life [27].…”

Section: Reptile Search Algorithm (Rsa)mentioning

confidence: 99%

“…They are always used in the domain of machine learning and clustering algorithms. The results of the proposed method are compared with other methods such as Aquila Optimizer (AO) [32], Particle Swarm Optimizer (PSO) [33], Grey Wolf Optimizer (GWO) [34], African Vultures Optimization Algorithm (AVOA) [35], Whale Optimization Algorithm (WOA) [36], Reptile Search Algorithm (RSA) [27], Remora Optimization Algorithm (ROA) [28], Arithmetic Optimization Algorithm (AOA) [37], and the proposed HRSA method. All the tested methods are tuned according to the original paper and its parameters.…”

Section: Data Clustering: Experimentsmentioning

confidence: 99%

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Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Almotairi

Abualigah

2022

Symmetry

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Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms’ search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems.

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Section: Reptile Search Algorithm (Rsa)mentioning

confidence: 99%

Section: Data Clustering: Experimentsmentioning

confidence: 99%

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Almotairi

Abualigah

2022

Symmetry

Self Cite

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“…It analyzes accurate Wind data from the Urban Region Energy Network. Several other techniques can be used to predict the power values [85][86][87]. An overview of applying deep learning techniques to renewable energy is presented in Table 3.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

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“…Furthermore, to satisfy the user QoS demands, the Q-learning method is employed to choose the best triggering points to minimize the impact of frequent undesirable handovers [ 31 ]. Other optimization techniques that can be used are founded in [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture

Abualigah

Huy

et al. 2022

Sensors

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Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.

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Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer

Cited by 934 publications

References 83 publications

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture

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